Hello! I'm a second-year PhD student at the University of Oxford, where I am exploring ways to make deep
algorithms more robust to common challenges in early-stage drug discovery.
This requires familiarity with both cutting-edge machine learning methods and the realities of practical drug discovery, which I have picked up through supervision by Yee Whye Teh (Oxford/DeepMind), Charlotte Deane (Oxford/Exscientia) and Garrett Morris (Oxford), as well as Torsten Schindler and Michael Reutlinger (Roche).
My research is funded by a Clarendon Scholarship (Oxford's flagship academic merit scholarship for graduate students) and additional awards from Brasenose College and Roche (Open Innovation Partnership).
Before moving to Oxford, I earned a BSc. in Interdisciplinary Sciences (chemistry, biology and CS) from ETH Zurich, where I was supervised by Gisbert Schneider and Simone Schürle-Finke.
If you would like to talk about research, feel free to reach out via email, Twitter, or LinkedIn!
Metropolis Sampling for Constrained Diffusion Models
Nic Fishman, Leo Klarner, Emile Mathieu, Michael Hutchinson, Valentin de Bortoli
GAUCHE: A Library for Gaussian Processes in Chemistry
Ryan-Rhys Griffiths*, Leo Klarner*, Henry B. Moss*, Aditya Ravuri*, et al.
Diffusion Models for Constrained Domains
Nic Fishman, Leo Klarner, Valentin De Bortoli, Emile Mathieu, Michael Hutchinson
Drug Discovery under Covariate Shift with Domain-Informed Prior Distributions over Functions
Leo Klarner, Tim G. J. Rudner, Michael Reutlinger, Torsten Schindler, Garrett M. Morris, Charlotte M. Deane, Yee Whye Teh
Bias in the Benchmark: Systematic Experimental Errors in Bioactivity Databases Confound Multi-task and Meta-learning Algorithms
Leo Klarner, Michael Reutlinger, Torsten Schindler, Charlotte M. Deane, Garrett M. Morris
5th AI in Chemistry Conference, Best Poster Award
Oxford Stats Graduate Poster Session, Best Poster Award
ICML 2022 AI4Science Workshop